/// <summary>
/// Main methods for model training
/// </summary>
/// <param name="mlconfigPath"></param>
/// <param name="token"></param>
/// <param name="trainingProgress"></param>
/// <param name="pdevice"></param>
/// <returns></returns>
public static TrainResult TrainModel(string mlconfigPath, CancellationToken token, TrainingProgress trainingProgress, ProcessDevice pdevice)
{
try
{
//device definition
DeviceDescriptor device = MLFactory.GetDevice(pdevice);
//LOad ML configuration file
var dicMParameters = MLFactory.LoadMLConfiguration(mlconfigPath);
//add path of model folder
dicMParameters.Add("root", Project.GetMLConfigFolder(mlconfigPath));
//prepare NN data
var retVal = MLFactory.PrepareNNData(dicMParameters, CustomNNModels.CustomModelCallEntryPoint, device);
//create trainer
MLTrainerEx tr = new MLTrainerEx(retVal.f.StreamConfigurations, retVal.f.InputVariables, retVal.f.OutputVariables);
//setup model checkpoint
string modelCheckPoint = null;
if (dicMParameters.ContainsKey("configid"))
{
modelCheckPoint = MLFactory.GetModelCheckPointPath(mlconfigPath, dicMParameters["configid"].Trim(' '));
}
//setup model checkpoint
string historyPath = null;
if (dicMParameters.ContainsKey("configid"))
{
historyPath = MLFactory.GetTrainingHistoryPath(mlconfigPath, dicMParameters["configid"].Trim(' '));
}
//create trainer
var trainer = tr.CreateTrainer(retVal.nnModel, retVal.lrData, retVal.trData, modelCheckPoint, historyPath);
//perform training
var result = tr.Train(trainer, retVal.nnModel, retVal.trData, retVal.mbs, device, token, trainingProgress, modelCheckPoint, historyPath);
return(result);
}
catch (Exception)
{
throw;
}
}
/// <summary>
/// Main method to perform training process
/// </summary>
/// <param name="strFilePath">ML configuration file</param>
/// <param name="device">Device of computation (GPU/CPU)</param>
/// <param name="token">Cancellation token for interrupting training process on user request.</param>
/// <param name="trainingProgress">training progress object</param>
/// <param name="customModel">custom neural network model if available</param>
public static TrainResult Run(string mlconfigPath, DeviceDescriptor device, CancellationToken token, TrainingProgress trainingProgress, CreateCustomModel customModel = null)
{
//LOad ML configuration file
var dicMParameters = MLFactory.LoadMLConfiguration(mlconfigPath);
var fi = new FileInfo(mlconfigPath);
var folderPath = MLFactory.GetMLConfigFolder(fi.FullName);
//add path of model folder
dicMParameters.Add("root", folderPath);
var retVal = MLFactory.PrepareNNData(dicMParameters, customModel, device);
//create trainer
MLTrainerEx tr = new MLTrainerEx(retVal.f.StreamConfigurations, retVal.f.InputVariables, retVal.f.OutputVariables);
//setup model checkpoint
string modelCheckPoint = null;
if (dicMParameters.ContainsKey("configid"))
{
modelCheckPoint = MLFactory.GetModelCheckPointPath(mlconfigPath, dicMParameters["configid"].Trim(' '));
}
//setup history of training path
//TODO
//create trainer
var trainer = tr.CreateTrainer(retVal.nnModel, retVal.lrData, retVal.trData, modelCheckPoint,null);
//perform training
var result = tr.Train(trainer, retVal.nnModel, retVal.trData, retVal.mbs, device, token, trainingProgress, modelCheckPoint, null);
//delete previous best model before change variable values
retVal.trData.LastBestModel = MLFactory.ReplaceBestModel(retVal.trData, mlconfigPath, result.BestModelFile);
//save best model to mlconifg file
var trStrData = retVal.trData.ToString();
var d = new Dictionary<string, string>();
d.Add( "training", trStrData );
//save to file
MLFactory.SaveMLConfiguration(mlconfigPath, d);
return result;
}